Key Responsibilities:
- Maintain and improve data ingestion and processing pipelines for scalability, reproducibility, reliability, and speed
- Improve the training and inference speeds of a large class of ML models including transformer-based architectures, structured state-space models, graph neural networks, and many others
- Design, implement, and deploy ways to make complex deep learning models usable by biologists and other domain specialists
- Manage ML infrastructure, both on premises and in the cloud
- Rapidly prototype POCs in partnership with ML scientists and other stakeholders
- Test models at scale and ensure CI/CD best practices are followed by the whole team
Basic Qualifications:
- 3+ years of experience
- Expertise in ML frameworks (e.g., PyTorch, TensorFlow)
- Strong background in distributed systems and containerization
- Self-motivated problem-solver with excellent communication skills
- Adaptability to evolving requirements in a fast-paced field
- Ability to mentor and elevate team members' skills
Preferred Qualifications:
- Previous startup experience bringing ML prototypes to production
- Familiarity with bioinformatics data/pipelines
- Knowledge of MLFlow, Kubeflow, Ray, Dask, Fluidstack, or similar tools
What we offer:
- A highly competitive salary, a meaningful equity ownership stake in Deep Genomics, and exceptional opportunities for learning and growth
- Company paid benefits
- Exceptional opportunities for learning and growth in a world-class team of researchers and software developers, working at the intersection of the most exciting areas of science and technology
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